基于超混沌的RBF神经网络图像自适应水印算法
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  • 英文篇名:Image Adaptive Watermarking Algorithm Based on Hyper-chaotic RBF Neural Network
  • 作者:杨树国 ; 刘庆亮 ; 熊鹏程
  • 英文作者:YANG Shuguo;LIU Qingliang;XIONG Pengcheng;College of Mathematics and Physics,Qingdao University of Science and Technology;A9Central Economic Search Department Amazon.com Inc;
  • 关键词:超混沌 ; RBF神经网络 ; JND模型 ; 盲检测
  • 英文关键词:hyper-chaotic;;RBF neural network;;JND model;;blind detection
  • 中文刊名:QDHG
  • 英文刊名:Journal of Qingdao University of Science and Technology(Natural Science Edition)
  • 机构:青岛科技大学数理学院;亚马逊A9中央经济搜索部;
  • 出版日期:2018-09-19
  • 出版单位:青岛科技大学学报(自然科学版)
  • 年:2018
  • 期:v.39;No.174
  • 基金:山东省教育科学“十二五”规划课题项目(YBS15014);; 山东省重点研发计划项目(2015GGX101020);; 青岛市科技发展计划项目(KJZD-13-27-JCH);; 山东省研究生教育创新计划项目(SDYY16010)
  • 语种:中文;
  • 页:QDHG201805017
  • 页数:6
  • CN:05
  • ISSN:37-1419/N
  • 分类号:110-114+122
摘要
为了更好地保护图像版权信息,本工作结合超混沌系统和RBF神经网络,提出一种基于超混沌的RBF神经网络模型加密算法,根据JND(最小可视觉误差)模型实现水印的自适应嵌入,并应用RBF神经网络进行水印信息盲检测。实验研究表明:该算法复杂度较低、容易实现,并且具有良好的安全性、不可感知性和鲁棒性。
        In order to better protect the image copyright,this paper proposes a new algorithm based on hyper-chaotic and RBF neural network model encryption algorithm,which is based on JND(minimum visual error)model to realize adaptive embedding of watermark,using RBF neural network to realize blind detection of watermark information.Experimental results show that the algorithm is low in complexity,easy to implement.And it also has good security,imperceptibility and robustness.
引文
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